SOTAVerified

Object Recognition

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

( Image credit: Tensorflow Object Detection API )

Papers

Showing 11511175 of 2042 papers

TitleStatusHype
(RF)^2 -- Random Forest Random Field0
RIDE: Reversal Invariant Descriptor Enhancement0
RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression0
RMNv2: Reduced Mobilenet V2 for CIFAR100
Robot In a Room: Toward Perfect Object Recognition in Closed Environments0
Robust Domain Generalization for Multi-modal Object Recognition0
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras0
Robustness of Humans and Machines on Object Recognition with Extreme Image Transformations0
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers0
Robust Shape Regularity Criteria for Superpixel Evaluation0
Robust Visual Knowledge Transfer via EDA0
Rock Hunting With Martian Machine Vision0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images0
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition0
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud0
Rotation-invariant shipwreck recognition with forward-looking sonar0
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification0
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks0
Saliency Driven Object recognition in egocentric videos with deep CNN0
Saliency for Fine-grained Object Recognition in Domains with Scarce Training Data0
Saliency for free: Saliency prediction as a side-effect of object recognition0
Salient Explanation for Fine-grained Classification0
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified